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Machine Learning and RF Spectrum Intelligence Gathering

2018-10-08T14:35:37+00:00

Many applications that are central to RF spectrum intelligence gathering require some sort of pattern recognition. For example, to classify a signal by type we need to identify the particular pattern associated with the modulation, while to recognise that there is an interesting signal present in received data, we need to distinguish between pattern and noise.

In this White Paper, we explore how machine learning techniques can be applied to these applications of signal classification and anomaly detection to deliver faster and more effective performance to customers.

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